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Progress and prediction of multicomponent quantification in complex systems with practical LC-UV methods

Complex systems exist widely, including medicines from natural products, functional foods, and biological samples. The biological activity of complex systems is often the result of the synergistic effect of multiple components. In the quality evaluation of complex samples, multicomponent quantitativ...

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Detalles Bibliográficos
Autores principales: Chen, Xi, Yang, Zhao, Xu, Yang, Liu, Zhe, Liu, Yanfang, Dai, Yuntao, Chen, Shilin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Xi'an Jiaotong University 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9999300/
https://www.ncbi.nlm.nih.gov/pubmed/36908853
http://dx.doi.org/10.1016/j.jpha.2022.11.011
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author Chen, Xi
Yang, Zhao
Xu, Yang
Liu, Zhe
Liu, Yanfang
Dai, Yuntao
Chen, Shilin
author_facet Chen, Xi
Yang, Zhao
Xu, Yang
Liu, Zhe
Liu, Yanfang
Dai, Yuntao
Chen, Shilin
author_sort Chen, Xi
collection PubMed
description Complex systems exist widely, including medicines from natural products, functional foods, and biological samples. The biological activity of complex systems is often the result of the synergistic effect of multiple components. In the quality evaluation of complex samples, multicomponent quantitative analysis (MCQA) is usually needed. To overcome the difficulty in obtaining standard products, scholars have proposed achieving MCQA through the “single standard to determine multiple components (SSDMC)” approach. This method has been used in the determination of multicomponent content in natural source drugs and the analysis of impurities in chemical drugs and has been included in the Chinese Pharmacopoeia. Depending on a convenient (ultra) high-performance liquid chromatography method, how can the repeatability and robustness of the MCQA method be improved? How can the chromatography conditions be optimized to improve the number of quantitative components? How can computer software technology be introduced to improve the efficiency of multicomponent analysis (MCA)? These are the key problems that remain to be solved in practical MCQA. First, this review article summarizes the calculation methods of relative correction factors in the SSDMC approach in the past five years, as well as the method robustness and accuracy evaluation. Second, it also summarizes methods to improve peak capacity and quantitative accuracy in MCA, including column selection and two-dimensional chromatographic analysis technology. Finally, computer software technologies for predicting chromatographic conditions and analytical parameters are introduced, which provides an idea for intelligent method development in MCA. This paper aims to provide methodological ideas for the improvement of complex system analysis, especially MCQA.
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spelling pubmed-99993002023-03-11 Progress and prediction of multicomponent quantification in complex systems with practical LC-UV methods Chen, Xi Yang, Zhao Xu, Yang Liu, Zhe Liu, Yanfang Dai, Yuntao Chen, Shilin J Pharm Anal Review Paper Complex systems exist widely, including medicines from natural products, functional foods, and biological samples. The biological activity of complex systems is often the result of the synergistic effect of multiple components. In the quality evaluation of complex samples, multicomponent quantitative analysis (MCQA) is usually needed. To overcome the difficulty in obtaining standard products, scholars have proposed achieving MCQA through the “single standard to determine multiple components (SSDMC)” approach. This method has been used in the determination of multicomponent content in natural source drugs and the analysis of impurities in chemical drugs and has been included in the Chinese Pharmacopoeia. Depending on a convenient (ultra) high-performance liquid chromatography method, how can the repeatability and robustness of the MCQA method be improved? How can the chromatography conditions be optimized to improve the number of quantitative components? How can computer software technology be introduced to improve the efficiency of multicomponent analysis (MCA)? These are the key problems that remain to be solved in practical MCQA. First, this review article summarizes the calculation methods of relative correction factors in the SSDMC approach in the past five years, as well as the method robustness and accuracy evaluation. Second, it also summarizes methods to improve peak capacity and quantitative accuracy in MCA, including column selection and two-dimensional chromatographic analysis technology. Finally, computer software technologies for predicting chromatographic conditions and analytical parameters are introduced, which provides an idea for intelligent method development in MCA. This paper aims to provide methodological ideas for the improvement of complex system analysis, especially MCQA. Xi'an Jiaotong University 2023-02 2022-12-05 /pmc/articles/PMC9999300/ /pubmed/36908853 http://dx.doi.org/10.1016/j.jpha.2022.11.011 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Review Paper
Chen, Xi
Yang, Zhao
Xu, Yang
Liu, Zhe
Liu, Yanfang
Dai, Yuntao
Chen, Shilin
Progress and prediction of multicomponent quantification in complex systems with practical LC-UV methods
title Progress and prediction of multicomponent quantification in complex systems with practical LC-UV methods
title_full Progress and prediction of multicomponent quantification in complex systems with practical LC-UV methods
title_fullStr Progress and prediction of multicomponent quantification in complex systems with practical LC-UV methods
title_full_unstemmed Progress and prediction of multicomponent quantification in complex systems with practical LC-UV methods
title_short Progress and prediction of multicomponent quantification in complex systems with practical LC-UV methods
title_sort progress and prediction of multicomponent quantification in complex systems with practical lc-uv methods
topic Review Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9999300/
https://www.ncbi.nlm.nih.gov/pubmed/36908853
http://dx.doi.org/10.1016/j.jpha.2022.11.011
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